Ecological niches are thought to comprise complex nonlinear functions of multiple measurable traits [1]}, [2]}, [3]}, [4]}. A central challenge in modeling the niche is thus to identify composites of traits that map to interpretable ecological roles, or the `soft properties' [5]} that summarize an organism's functional capabilities.
A powerful analysis method for meeting this challenge is offered by diffusion maps [6]}, [7]}.
This mathematically simple manifold learning method leverages the physical relationship between diffusion processes and geometry to define a new coordinate system for a dataset, where the axes, or variables, are nonlinear composites of its major features. While the mathematical procedure does not provide an interpretation of these variables, our analyses show that they correspond to meaningful metabolic strategies. This offers a potential bridge between ecological niche theories and information that is readily accessible from annotated bacterial genomes.
